Image detection scanning method for object surface defects and image detection scanning system thereof

ABSTRACT

An image detection scanning method for object surface defects and an image detection scanning system thereof are provided. The method includes capturing a test image by a photosensitive element according to test light, determining whether a setting parameter of the photosensitive element is normal by a processor according to the test image, generating a warning signal if the setting parameter is abnormal, performing a detection procedure if the setting parameter is normal, sequentially moving one of a plurality of areas on a surface of an object to the detection position in the detection procedure, providing detection light by a light source component in the detection procedure to illuminate the detection position, and capturing a detection image of each of the areas sequentially located at the detection position by the photosensitive element according to the detection light in the detection procedure.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority from U.S. Patent Application Ser. No.62/848,216, filed on May 15, 2019, the entire disclosure of which ishereby incorporated by reference.

BACKGROUND OF THE INVENTION Field of the Invention

The present disclosure relates to an image detection scanning method forobject surface defects and an image detection scanning system thereof.

Description of the Prior Art

The defect detection for products is a critical step in the industrialmanufacturing process. Defective products cannot be sold; otherwisemalfunctioning end products can be resulted if defective intermediateproducts are sold to other manufacturers for processing. Oneconventional defect detection method is manually inspecting a productunder test by naked eyes or by touching of hands, so as to determinewhether a product contains defects, such as recesses, scratches, colordifferences or damages. However, manually inspecting whether a productcontains defects yields less satisfactory efficiency, and has a greaterprobability of misjudgment, leading to the problem of an unmanageableproduct yield rate.

SUMMARY OF THE INVENTION

The present disclosure provides an image detection scanning system forobject surface defects. The system includes a driver component, a lightsource component, a photosensitive element and a processor. The drivercomponent carries the object, and the surface of the object is dividedinto a plurality of areas along an extension direction. The drivercomponent sequentially moves one of the plurality of areas on thesurface to a detection position. The light source component isconfigured to face the detection position and provides detection lightto illuminate the detection position, wherein a light incident angle ofthe detection light relative to a normal line of the area located at thedetection position is less than or equal to 90 degrees. Thephotosensitive element is configured to face the detection position,captures a detection image of each of the areas sequentially located atthe detection position, and captures a test image according to testlight before capturing the detection image of the area, wherein aphotosensitive axis of the photosensitive element is parallel to thenormal line, or is between the normal line and the extension direction.The processor is coupled to the photosensitive element, and determineswhether a setting parameter of the photosensitive element is normalaccording to the test image, and if the setting parameter is abnormaland the photosensitive element has performed a calibration operationcorresponding to the setting parameter, the processor generates awarning signal.

The present disclosure provides an image detection scanning method forobject surface defects. The method includes: capturing a test image by aphotosensitive element according to test light; determining whether asetting parameter of the photosensitive element is normal by a processoraccording to the test image; generating a warning signal if the settingparameter is abnormal; performing a detection procedure if the settingparameter is normal; sequentially moving one of a plurality of areas ona surface of an object to the detection position by a driver componentin the detection procedure, wherein the areas are divided along anextension direction of the surface; providing detection light by a lightsource component facing the detection position in the detectionprocedure, wherein the detection light illuminates the detectionposition with a light incident angle of less than or equal to 90 degreesrelative to a normal line of the area located at the detection position;configuring the photosensitive element to face the detection position inthe detection procedure, wherein a photosensitive axis of thephotosensitive element is parallel to the normal line or is between thenormal line and the extension direction; and capturing a detection imageof each of the areas sequentially located at the detection position bythe photosensitive element according to the detection light in thedetection procedure.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of an image detection scanning system forobject surface defects according to an embodiment of the presentdisclosure;

FIG. 2 is a function block diagram of the image detection scanningsystem for object surface defects according to an embodiment;

FIG. 3 is a schematic diagram of an implementation form of relativeoptical positions of the object, the light source component and thephotosensitive element in FIG. 1;

FIG. 4 is a schematic diagram of another implementation form of relativeoptical positions of the object, the light source component and thephotosensitive element in FIG. 1;

FIG. 5 is a schematic diagram of yet another implementation form ofrelative optical positions of the object, the light source component andthe photosensitive element in FIG. 1;

FIG. 6 is a flowchart of an image detection scanning method for objectsurface defects according to an embodiment of the present disclosure;

FIG. 7 is a schematic diagram of a predetermined surface defectcorresponding to the object in FIG. 1 according to an embodiment;

FIG. 8 is a schematic diagram of an image detection scanning system forobject surface defects according to another embodiment of the presentdisclosure;

FIG. 9 is a schematic diagram of an implementation form of relativeoptical positions of the object, the light source component and thephotosensitive element in FIG. 7;

FIG. 10 is a schematic diagram of another implementation form ofrelative optical positions of the object, the light source component andthe photosensitive element in FIG. 7;

FIG. 11 is a flowchart of an image detection scanning method for objectsurface defects according to another embodiment of the presentdisclosure;

FIG. 12 is a schematic diagram of an object image including sub objectimages according to an embodiment; and

FIG. 13 is a schematic diagram of one object image in FIG. 12 accordingto an embodiment.

DETAILED DESCRIPTION OF THE EMBODIMENTS

FIG. 1 shows a schematic diagram of an image detection scanning systemfor object surface defects according to an embodiment of the presentdisclosure. Referring to FIG. 1, the image detection scanning system forobject surface defects is suitable for scanning an object 2 so as toobtain at least one detection image of the object 2. In someembodiments, the surface of the object 2 may include at least onesurface defect, and the corresponding detection image also presents animage block with the surface defect. Herein, the surface defect is athree-dimensional structure. Herein, the three-dimensional structure isin a scale of mm to μm.

The image detection scanning system for object surface defects includesa driver component 11, a light source component 12 and a photosensitiveelement 13. Referring to FIG. 1 to FIG. 4, FIG. 2 shows a function blockdiagram of an image detection scanning system for object surface defectsin FIG. 1 according to an embodiment, and FIGS. 3 and 4 respectivelyshow schematic diagrams of two implementation forms of relative opticalpositions of the object 2, the light source component 12 and thephotosensitive element 13 in FIG. 1. The light source component 12 andthe photosensitive element 13 face a detection position 14 on the drivercomponent 11. The driver component 11 carries the object 2 underdetection. The object 2 has a surface 21 which is divided into aplurality of areas along an extension direction D1 of the surface 21 ofthe object 2. Herein, the surface 21 of the object 2 in FIG. 3 and FIG.4 are, for example but not limited to, divided into nine areas, of whichthree areas 21A to 21C are denoted. The surface 21 of the object 2 canalso be divided into areas in other quantities.

Referring to FIGS. 1 to 6, FIG. 6 shows a flowchart of an imagedetection scanning method for object surface defects according to anembodiment of the present disclosure. The image detection scanningsystem for object surface defects can perform a detection procedure. Thedriver component 11 sequentially moves one of the plurality of areas 21Ato 21C on the surface 21 to the foregoing detection position 14 (stepS01) in the detection procedure. The light source component 12 emitslight L1 (hereinafter referred to as detection light L1) to the areas21A to 21C located at the detection position 14 (step S02) in thedetection procedure. Furthermore, as shown in FIG. 3 and FIG. 4, anincluded angle (hereinafter referred to as a light incident angle θ)between the incident direction of the detection light L1 and a normalline 14A of each of the areas 21A to 21C located at the detectionposition 14 is more than 0 degree and less than or equal to 90 degrees;that is to say, the detection light L1 illuminates the detectionposition 14 with a light incident angle θ of more than 0 degree and lessthan or equal to 90 degrees relative to the normal line 14A (step S03),such that the areas 21A to 21C on the surface 21 are sequentiallyilluminated at the detection position 14 by the detection light L1 froma lateral or inclined direction. Moreover, the areas 21A to 21C on thesurface 21 sequentially face the photosensitive element 13. In someembodiments, as shown in FIGS. 3 and 4, the photosensitive axis 13A ofthe photosensitive element 13 is parallel to the normal line 14A;alternatively, as shown in FIG. 5, the photosensitive axis 13A of thephotosensitive element 13 is between the normal line 14A and theextension direction D1, that is to say, an included angle α is presentbetween the photosensitive axis 13A of the photosensitive element 13 andthe normal line 14A. The photosensitive element 13 receives diffusedlight generated by the light received on the areas 21A to 21C on thesurface 21, and captures, according to the diffused light, detectionimages of the areas 21A to 21C sequentially located at the detectionposition 14 (step S04).

For instance, taking the driver component 11 first moving the area 21Ato the detection position 14 in step S01 for example, the detectionlight L1 provided by the light source component 12 illuminates the area21A, the photosensitive element 13 captures the detection image of thearea 21A in step S04, the driver component 11 next moves the object 2,the driver component 11 again moves the area 21B to the detectionposition 14 in step S01, the photosensitive element 13 again capturesthe detection image of the area 21B in step S04, the driver component 11next moves the object 2, the driver component 11 again moves the area21C to the detection position 14 in step S01, and the photosensitiveelement 13 again captures the detection image of the area 21C in stepS04.

Thus, according to the light incident angle θ of more than 0 degree andless than or equal to 90 degrees, that is, according to the detectionlight L1 that is laterally incident or incident at an inclination, ifthe surface 21 of the object 2 includes a groove-shaped or hole-shapedsurface defect, the detection light L1 cannot illuminate the bottom ofthe surface defect and the surface defect appears as a shadow in thedetection images of the areas 21A to 21C on the surface 21. Hence, thesurface 21 and the surface defect form an apparent contrast in thedetection images, and the image detection scanning system for objectsurface defects or an inspector can determine whether the surface 21includes a surface defect according to whether the detection imageincludes a shadow (step S06), and the inspector is not required todetermine by observing the object 2 with naked eyes or touching theobject 2 with hands whether the surface 21 of the object 2 includes asurface defect, thereby significantly enhancing the efficiency ofdetection for surface defects as well as alleviating the issue of humanmisjudgment.

In one embodiment, the photosensitive element 13 can be implemented by alinear image sensor or a planar image sensor. Furthermore, the detectionlight L1 provided by the light source component 12 can be visible light,the light wavelength of the detection light L1 can range between 380 nmand 750 nm, for example, violet light having a light wavelength rangingbetween 380 nm and 450 nm to red light having a light wavelength rangingbetween 620 nm and 750 nm. The visible light allows surface defects onthe surface 21 in a scale of μm to form an image in the detection image.

In one embodiment, according to different light incident angles θ,surface defects of different depths present different brightness levelsin the detection image. More specifically, as shown in FIG. 4, when thelight incident angle θ is equal to 90 degrees, the incident direction ofthe detection light L1 is perpendicular to a depth direction D2 of thesurface defect, and regardless of the depth of the surface defect, thesurface defect on the surface 21 is not illuminated by the detectionlight L1 due to being recessed and hence does not produce any reflectedlight or diffused light; more specifically, surface defects with deeperor shallower depths both present shadows in the detection image, suchthat the detection image has a less obvious contrast or even nearly nocontrast. As shown in FIG. 3, when the light incident angle θ is lessthan 90 degrees, the incident direction of the detection light L1 is notperpendicular to the depth direction D2 of the surface defect, thedetection light L1 illuminates partial regions of the surface defectbelow the surface 21, the partial regions of the surface defect receiveillumination of the detection light L1 and produce reflected light anddiffused light, and the photosensitive element 13 receives the reflectedlight and diffused light from the partial regions of the surface defect;more specifically, surface defects present images with brighter borders(for protruding defects, for example) or darker borders (for recesseddefects, for example) in the detection image, such that the detectionimage has a better contrast.

Moreover, in a situation where the same light incident angle θ is lessthan 90 degrees, the photosensitive element 13 receives more reflectedlight and diffused light from shallower surface defects than from deepersurface defects, and compared to a surface defect having a greaterdepth-width ratio, a shallower surface defect presents a brighter imagein the detection image. Furthermore, in a situation where the lightincident angle θ is less than 90 degrees, as the light incident angle θgets smaller, more reflected light and diffused light are produced inthe surface defects, the surface defects present brighter images in thedetection image, and the brightness presented by a shallower surfacedefect in the detection image is also greater than the brightnesspresented by a deeper surface defect in the detection image. Forexample, a 30-degree light incident angle θ is less than a 60-degreelight incident angle θ. Compared to a detection image corresponding tothe 60-degree light incident angle θ, the surface defect presents higherbrightness in a detection image corresponding to the 30-degree lightincident angle θ. In addition, in the detection image corresponding tothe 30-degree light incident angle θ, a shallower surface defectpresents higher brightness in the detection image compared to a deepersurface defect.

On this basis, the value of the light incident angle θ and thebrightness presented by a surface defect in a detection image have anegative correlation relationship. As the light incident angle θ getssmaller, a shallower surface defect presents a brighter image in adetection image; that is to say, in a situation where the light incidentangle θ is smaller, it becomes more difficult for the image detectionscanning system for object surface defects or the inspector to identifya shallower surface defect, but it is easier for the image detectionscanning system for object surface defects or the inspector to identifya deeper surface defect according to a darker image. In contrast, as thelight incident angle θ becomes larger, both of a deeper surface defectand a shallower surface defect present darker images in a detectionimage; that is to say, the image detection scanning system for objectsurface defects or the inspector is capable of identifying all surfacedefects in a situation where the light incident angle θ is larger.

Hence, the image detection scanning system for object surface defects orthe inspector can set the corresponding light incident angle θ accordingto a predetermined hole depth d of a predetermined surface defect to bedetected and the described negative correlation relationship. As shownin FIG. 1 and FIG. 2, the image detection scanning system for objectsurface defects further includes a light source adjustment component 16coupled to the light source component 12. If detection for apredetermined deeper surface defect but not a predetermined shallowersurface defect is desired, the light source adjustment component 16 canadjust the position of the light source component 12 according to thelight incident angle θ calculated by using the described negativecorrelation relationship and thus set a smaller light incident angle θin step S03. Furthermore, the light source adjustment component 16drives the light source component 12 to output the detection light L1,such that the predetermined shallower surface defect presents a brighterimage in the detection image and the predetermined deeper surface defectpresents a darker image in the detection image. If detection for bothpredetermined shallower and deeper surface defects is desired, the lightsource adjustment component 16 can adjust the position of the lightsource component 12 according to the light incident angle θ calculatedby using the described negative correlation relationship and thus set alarger light incident angle θ (e.g., 90 degrees) in step S03. Further,the light source adjustment component 16 drives the light sourcecomponent 12 to output the detection light L1, such that thepredetermined shallower and deeper surface defects both present shadowsin the detection image.

For example, assuming that the object 2 is applied to a spindle of asafety belt component of an automobile, the foregoing surface defect canbe a sand hole or an air hole caused by dust or air during themanufacturing process of the object 2, or a bump or a scratch, whereinthe depth of the sand hole or the air hole is greater than that of thebump or the scratch. If detection for determining whether the object 2contains sand holes or air holes is desired but detection fordetermining whether the object 2 contains bumps or scratches is notneeded, the light source adjustment component 16 can adjust the positionof the light source component 12 according to the light incident angle θcalculated by using the described negative correlation relationship andthus set a smaller light incident angle θ in step S03. Accordingly, asand hole or an air hole presents lower brightness in the detectionimage, whereas a bump or a scratch presents higher brightness in thedetection image, and the image detection scanning system for objectsurface defects or the inspector can quickly identify whether the object2 contains sand holes or air holes. If detection for determining whetherthe object 2 contains bumps, scratches, sand holes and air holes isdesired, the light source adjustment component 16 can adjust theposition of the light source component 12 according to the lightincident angle θ calculated by using the described negative correlationrelationship and thus set a larger light incident angle θ in step S03.Accordingly, bumps, scratches, sand holes and air holes all presentshadows in the detection image.

In one embodiment, the light incident angle θ is associated with apredetermined aspect ratio of a predetermined surface defect to bedetected. As shown in FIG. 7, taking the predetermined surface defecthaving a predetermined hole depth d and a predetermined hole radius rfor example, the predetermined hole radius r is the distance between anylateral surface within the predetermined surface defect and the normalline 14A, and a ratio (r/d) of the predetermined hole radius r to thepredetermined hole depth d is the aspect ratio (r/d) stated above, andthe light incident angle θ is the arctangent (r/d). On this basis, thelight source adjustment component 16 can adjust the position of thelight source component 12 according to the aspect ratio (r/d) of thepredetermined surface defect to be detected and thus set a thresholdangle of the light incident angle θ as the arctangent (r/d) in step S03,wherein the light incident angle θ needs to satisfy the conditions ofbeing equal to or more than the arctangent (r/d) and less than or equalto 90 degrees, and the light source adjustment component 16 drives thelight source component 12 to output the detection light L1 afteradjusting the position of the light source component 12. In someembodiments, the predetermined hole radius r can be set in advanceaccording to the size of the surface defect expected to be detected atthe object 2.

In one embodiment, as shown in FIG. 1 and FIG. 2, the image detectionscanning system for object surface defects further includes a processor15. The processor 15, coupled to the light source adjustment component16, is capable of calculating the light incident angle θ according tothe described negative correlation relationship and the arctangent(r/d), and then drives the light source adjustment component 16 toadjust the position of the light source component 12 according to thelight incident angle θ calculated.

In one embodiment, as shown in FIG. 1, FIG. 3 and FIG. 4, the object 2is cylindrical, and the surface 21 of the object 2 can be a lateralsurface of the object 2; that is, the surface 21 is a cylindricalsurface and has a radian of 2 π. Herein, as shown in FIG. 1 and FIG. 2,the driver component 11 includes a carrier element 111 and a step motor112 connected to the carrier element 111. The carrier element 111carries the object 2. In step S01, the step motor 112 can rotate in aclockwise direction or a counterclockwise direction, and drives thecarrier element 111 to rotate so as to impel the object 2 to rotate.Taking the surface 21 including the areas 21A to 21C for example, thestep motor 112 can sequentially rotate by 120 degrees, so as to move theareas 21A to 21C to the detection position 14. In another embodiment, asshown in FIG. 8 to FIG. 10, the object 2 is a cuboid or a cube in shape,and the surface 21 of the object 2 can be a non-curve surface having acurvature equal to zero or approximating zero. Herein, along anextension direction D1 of the surface 21 of the object 2, the surface 21can be divided into the areas 21A to 21C. In step S01, the step motor112 can drive the carrier element 111 to move parallel along theextension direction D1 perpendicular to the normal line 14A, so as tomove the areas 21A to 21C to the detection position 14.

In some embodiments, the extension direction D1 can be acounterclockwise direction or a clockwise direction relative to thecentral axis of the object 2 as a rotation axis as shown in FIG. 1, orthe long axis direction of the object 2 as shown in FIG. 8.

In one embodiment, as shown in FIG. 1 and FIG. 8, the light sourcecomponent 12 can include a light emitting element. In anotherembodiment, as shown in FIGS. 3, 4, 9 and 10, the light source component12 can include two light emitting elements 121 and 122, and the twolight emitting elements 121 and 122 are symmetrically arranged on twoopposite sides of the object 2 relative to the normal line 14A. The twolight emitting elements 121 and 122 individually illuminate thedetection position 14, the surface 21 is illuminated by the symmetricaldetection light L1 and symmetrical diffused light is thus produced, andthe photosensitive element 13 sequentially captures the detection imagesof the areas 21A to 21C located at the detection position 14 accordingto the symmetrical diffused light, hence enhancing the imaging qualityof the detection image. In some embodiments, the light emitting elements121 and 122 can be implemented by one or more light emitting diodes; insome embodiments, each of the light emitting elements 121 and 122 can beimplemented by a laser light source.

In conclusion of the description above, according to the image detectionscanning system for object surface defects and the image detectionscanning method for object surface defects in one embodiment of thepresent disclosure, the image detection scanning system for objectsurface defects is capable of generating a detection image of an objectsurface, the image detection scanning system for object surface defectsor an inspector can determine according to the detection image whetherthe surface of the object contains a surface defect, and the inspectoris not required to determine by observing with naked eyes or touchingwith hands whether the surface of the object contains a surface defect,thus significantly enhancing the efficiency of detection for surfacedefects as well as alleviating the issue of human misjudgment.Furthermore, the light incident angle is associated with a predeterminedhole depth or a predetermined aspect ratio of a predetermined surfacedefect to be detected, and the image detection scanning system forobject surface defects or the inspector can adjust the light incidentangle such that the surface defect presents corresponding brightness inthe detection image, and the image detection scanning system for objectsurface defects or the inspector can then more efficiently identify thesurface defects to be detected according to detection images ofdifferent brightness levels.

In one embodiment, the image detection scanning system for objectsurface defects further includes a test procedure and a coincidenceprocedure. The image detection scanning system for object surfacedefects sequentially performs the test procedure, the coincidenceprocedure and the foregoing detection procedure. The photosensitiveelement 13 captures a test image according to test light in the testprocedure. In the coincidence procedure, the object 2 has apredetermined reference point, the driver component 11 moves the object2 to coincide the predetermined reference point of the object 2 to thedetection position 14, and after the predetermined reference point ofthe object 2 is coincided with the detection position 14, the drivercomponent 11 uses the predetermined reference point of the object 2 as astarting position and sequentially moves the areas 21A to 21C on thesurface 21 to the detection position 14.

More specifically, referring to FIGS. 1, 2, 8 and 11, the processor 15is coupled between the photosensitive element 13 and the drivercomponent 11. In the test procedure, the photosensitive element 13captures a test image (step S07), and the processor 15 receives the testimage captured by the photosensitive element 13, determines whether thetest image is normal (step S08), and accordingly determines whether todrive the driver component 11 to perform the coincidence procedure. Ifthe test image is normal, it means that the photosensitive element 13 iscapable of capturing a normal detection image in step S04 in thedetection procedure. The processor 15 generates an coincidence signalafter determining that the test image is normal (a determination resultof “yes”), and the driver component 11 receives the coincidence signalfrom the processor 15 and moves the object 2 to coincide the object 2with the detection position 14 according to the coincidence signal (stepS09). After the object 2 is coincided with the detection position 14,the image detection scanning system for object surface defects entersthe detection procedure, the driver component 11 sequentially moves theareas 21A to 21C to the detection position 14 in the detection procedure(step S01), the light source component 12 provides the detection lightL1 to illuminate the detection position 14 in the detection procedure(step S02 and step S03), and the photosensitive element 13 sequentiallycaptures the detection images of the areas 21A to 21C located at thedetection position 14 (step S04). The repeated details are omittedherein.

In one embodiment, as shown in FIG. 2, the processor 15 is coupled tothe step motor 112. In step S09, the processor 15 can send thecoincidence signal to control the step motor 112, such that the stepmotor 112 rotates to drive the carrier element 111 to move or rotate soas to coincide the object 2 with the detection position 14.

In one embodiment, after the photosensitive element 13 captures thedetection images of the areas 21A to 21C on the surface 21, theprocessor 15 receives from the photosensitive element 13 the detectionimages of the areas 21A to 21C on the surface 21 and combines thedetection images of the areas 21A to 21C on the surface 21 into anobject image (step S10), and the image detection scanning system forobject surface defects or the inspector can determine whether thesurface 21 of the object 2 in overall includes surface defects accordingto the object image generated from the combination of the detectionimages.

In one embodiment, referring to FIG. 8, one single photosensitiveelement 13 can be provided for the image detection scanning system forobject surface defects, and the photosensitive element 13 is used toperform image capturing of multiple areas 21A to 21C on the surface 21so as to obtain multiple detection images respectively corresponding tothe areas 21A to 21C on the surface 21. Then, the processor 15 combinesthe detection images of the areas 21A to 21C on the surface 21 into anobject image. In some embodiments, if the object 2 is cylindrical, theone single photosensitive element 13 can perform image capturing ofmultiple areas 21A to 21C of the middle section of the object 2 toobtain multiple detection images respectively corresponding to the areas21A to 21C, and the processor 15 then combines the detection images ofthe areas 21A to 21C into an object image 23, as shown in FIG. 13, whichshows an example of detection images 231, 232 and 233 of the three areas21A to 21C on the surface 21.

In one embodiment, as shown in FIG. 1, multiple photosensitive elements13 can be provided for the image detection scanning system for objectsurface defects, and the photosensitive elements 13 respectively capturedetection images of areas on a surface of the object 2 at differentsection positions located at the detection position 14. For example,taking a spindle as the object 2 for example, as shown in FIG. 1, oneend of the object 2 is a narrower structure, the photosensitive elements13 can be three in quantity, and the processor 15 forms an object imageof the object 2 according to the detection images captured by the threephotosensitive elements 13, as shown in FIG. 12. The object imageincludes a sub object image 22 (the upper section of the object image inFIG. 12) combined from the detection images of the areas 21A to 21Cobtained by the first photosensitive element of the three photosensitiveelements 13, a sub object image 23 (the middle section of the objectimage in FIG. 12) combined from the detection images of the areas 21A to21C obtained by the second photosensitive element of the threephotosensitive elements 13, and a sub object image 24 (the lower sectionof the object image in FIG. 12) combined from the detection images ofthe areas 21A to 21C obtained by the third photosensitive element of thethree photosensitive elements 13.

In one embodiment, the processor 15 can automatically determine,according to the object image generated from combining the detectionimages, whether the surface 21 of the object 2 contains surface defects,whether the surface 21 has different textures, and whether the surface21 contains an attachment such as paint or grease; that is, theprocessor 15 is capable of automatically determining different surfacepatterns of the object 2 according to the object image. Morespecifically, the processor 15 includes an artificial neural networksystem, which has a learning phase and a prediction phase. In thelearning phase, the object image inputted into the artificial neuralnetwork system contains known surface patterns, and after the objectimage with known surface pattern is inputted, the artificial neuralnetwork system performs deep learning according to the known surfacepatterns and the surface pattern type (hereinafter referred to as apredetermined surface pattern type) of the known surface defects tobuild a predictive model (formed by a plurality of hidden layerssequentially connected, wherein each hidden layer includes one or moreneurons, each of which performs a determination item). In other words,in the learning phase, the object image with known surface patterns isused to generate determination items of the neurons and/or to adjust aconnection weighting of any two neurons, such that a prediction result(i.e., the predetermined surface pattern type outputted) of each objectimage conforms to the known surface patterns.

For instance, taking a sand hole or an air hole, a bump or a scratch asthe foregoing surface defect as an example, image blocks presentingdifferent surface patterns can be image blocks with images of sand holesof different depths, image blocks without images of sand holes but withimages of bumps or scratches, image blocks with images of differentlevels of surface roughness, image blocks without images of surfacedefects, image blocks with images of surface defects with differentcontrasts and thus different aspect ratios as a result of illuminatingthe areas 21A to 21C by the detection light L1 of different lightwavelengths, or image blocks having attachments of different colors.Herein, the artificial neural network system performs deep learningaccording to the object images of different surface patterns describedabove, so as to build a predictive model for identifying various surfacepatterns. In addition, the artificial neural network system cancategorize object images having different surface patterns to generatein advance different predetermined surface pattern types. Thus, in theprediction phase, after the object image is generated and inputted intothe artificial neural network system, the artificial neural networksystem executes the foregoing predictive model according to the objectimage generated from combining the detection images, so as to identifythe object image presenting the surface pattern of the object 2 in theobject image (step S11). Moreover, the object image is categorized bythe predictive model, that is, the artificial neural network systemcategorizes the object image having the surface pattern of the object 2according to the plurality of predetermined surface pattern types (stepS12). At an output terminal, the object image undergoes percentileprediction according to the predetermined surface pattern types, thatis, the percentage of possibility of falling within the individual typesis predicted.

For instance, taking the areas 21A to 21C on the surface 21 for example,the artificial neural network system executes the predictive modelaccording to the combined object images of the areas 21A to 21C, and canuse the object image of the object 2 to identify that the area 21Acontains sand holes and bumps, the area 21B does not contain any surfacedefects, the area 21C contains sand holes and paint, and the surfaceroughness of the area 21A is greater than the surface roughness of thearea 21C. Next, assuming that there are six types of predeterminedsurface pattern types, namely, containing sand holes or air holes,containing scratches or bumps, having a high level of roughness, havinga low level of roughness, having an attachment, and without any surfacedefects, the artificial neural network system can categorize thedetection image of the area 21A to the predetermined types of containingsand holes or air holes and containing scratches or bumps, categorizethe detection image of the area 21B to the predetermined type of withoutany surface defects, and categorize the detection image of the area 21Cto the predetermined type of containing sand holes or air holes and thepredetermined type of having an attachment, and can further categorizethe detection image of the area 21A to the predetermined type of havinga high level of roughness, and categorize the detection images of theareas 21B and 21C to the predetermined type of having a low level ofroughness. Herein, by identifying different surface patterns using theartificial neural network system, the efficiency of detection issignificantly enhanced, and the probability of human misjudgment is alsoreduced.

In one embodiment, the deep learning of the foregoing artificial neuralnetwork system can be implemented by, for example but not limited to, aconvolutional neural network (CNN) algorithm.

In one embodiment, the photosensitive element 13 is the foregoing linearimage sensor that has a field of vision (FOV) of approximately 0 degree.After the processor 15 receives the detection images of the areas 21A to21C captured by the photosensitive element 13, in step S10, theprocessor 15 is not required to additionally perform image processingaccording to the detection images of the areas 21A to 21C but candirectly combine the detection images of the areas 21A to 21C into theobject image.

In another embodiment, the photosensitive element 13 is the foregoingplanar image sensor that has a field of view of approximately 5 degreesto 30 degrees. In the detection images of the areas 21A to 21C capturedby the photosensitive element 13, with regard to short sides of thedetection images, the middle regions of the detection images have betterimaging quality compared to other regions outside the middle regions.Herein, after the processor 15 receives the detection images of theareas 21A to 21C captured by the photosensitive element 13, theprocessor 15 captures the middle regions of the detection images on thebasis of the short sides of the detection images in step S10. Morespecifically, in step S10, the processor 15 can capture the middleregions of the detection images according to a predetermined viewingangle within the field of view of the planar image sensor. For example,the predetermined viewing angle can be 1 degree, and the processor 15captures the middle regions of the detection images corresponding to the1-degree predetermined viewing angle, e.g., the middle region having awidth of one pixel, and the processor 15 further combines the middleregions of the detection images into the object image, so as to avoidmerging other regions in the detection images having poorer imagingquality into the object image, further enhancing the accuracy of theartificial neural network system in identifying surface patterns of theobject 2.

In one embodiment, in step S07, the photosensitive element 13 cancapture the image of any of the areas 21A to 21C on the surface 21 asthe test image, and the processor 15 can compare whether the averagebrightness of the test image satisfies predetermined brightness in stepS08 to determine whether the test image is normal. If the averagebrightness of the test image does not satisfy the predeterminedbrightness (a determination result of “no”), it means that the testimage is abnormal, that is, the light incident angle θ set by the lightsource adjustment component 16 cannot accurately reflect thepredetermined surface pattern type to be detected. At this point, theimage detection scanning system for object surface defects performs acalibration procedure (step S05), and the processor 15 in thecalibration procedure controls the light source adjustment component 16to again adjust the position of the light source component 12 accordingto the described negative correlation relationship or the arctangent(r/d) to again set the light incident angle θ. The light sourceadjustment component 16 drives the light source component 12 to emitanother test light having a different light incident angle θ after againadjusting the position of the light source component 12, such that thephotosensitive element 13 captures the image of any one of the areas 21Ato 21C according to the another test light (step S07) to generateanother test image, and the processor 15 can again compare whether theaverage brightness of the another test image satisfies the foregoingpredetermined brightness (step S08). If the average brightness of theanother test image still does not satisfy the predetermined brightness(a determination result of “no”), the processor 15 controls the lightsource adjustment component 16 to again adjust the position of the lightsource component 12 and hence again adjust the light incident angle θ(step S05), until the average brightness of the test image captured bythe photosensitive element 13 satisfies the predetermined brightness. Ifthe average brightness of the test image satisfies the predeterminedbrightness, the processor 15 determines in step S08 that the test imageis normal (a determination result of “yes”), and the image detectionscanning system for object surface defects then performs subsequent stepS09 to perform the coincidence procedure and the detection proceduredescribed above.

In one embodiment, in step S08, the processor 15 can also determineaccording to the test image whether a setting parameter of thephotosensitive element 13 is normal. If the test image is normal (adetermination result of “yes”), it means that the setting parameter ofthe photosensitive element 13 is normal, and the processor 15 thengenerates the foregoing coincidence signal, such that the imagedetection scanning system for object surface defects sequentially entersthe coincidence procedure and the detection procedure described above.If the test image is abnormal (a determination result of “no”), it meansthat the setting parameter of the photosensitive element 13 is abnormal,and the processor 15 does not generate the foregoing coincidence signal.At this point, the processor 15 further determines whether thephotosensitive element 13 has performed a calibration operation of thesetting parameter thereof (step S13). If the photosensitive element 13has performed the calibration operation of the setting parameter thereof(a determination result of “yes”), the processor 15 generates a warningsignal indicating that the photosensitive element 13 is abnormal (stepS14); if the photosensitive element 13 has not yet performed thecalibration operation of the setting parameter thereof (a determinationresult of “no”), the image detection scanning system for object surfacedefects then enters the calibration procedure described above (stepS05), and the processor 15 drives the photosensitive element 13 toperform the calibration operation of the setting parameter thereof inthe calibration procedure. After the photosensitive element 13 hasperformed the calibration operation, the photosensitive element 13captures another test image in step S07, and the processor 15 againdetermines whether the another test image captured after thephotosensitive element 13 has performed the calibration operation isnormal (step S08). If the processor 15 determines that the another testimage is still abnormal (a determination result of “no”), the processor15 next determines in step S13 that the photosensitive element 13 hasperformed the calibration operation (a determination result of “yes”),and the processor 15 generates the warning signal indicating that thephotosensitive element 13 is abnormal in step S14.

In one embodiment, the image detection scanning system for objectsurface defects further includes an audio/video display unit; thewarning signal can include video, audio, or audio and video, and theaudio/video display unit can display the warning signal described above.Furthermore, the image detection scanning system for object surfacedefects can also have a networking function, and the processor 15 cansend the warning signal by the networking function to a cloud terminalfor storage or send the warning signal to another device by thenetworking function, such that the cloud terminal or a user of the otherdevice learns that the photosensitive element 13 is abnormal to furtherdebug the photosensitive element 13.

In one embodiment, the setting parameter of the photosensitive element13 includes a photosensitivity value, an exposure value, a focal lengthvalue, a contrast setting value, or any combination thereof. In stepS08, the processor 15 determines whether average brightness or contrastof the test image satisfies predetermined brightness to accordinglydetermine whether the above-mentioned setting parameters are normal. Forexample, if the average brightness of the test image does not satisfythe predetermined brightness, it means that one of the settingparameters of the photosensitive element 13 is incorrect, which rendersthe average brightness or contrast of the test image to not satisfy thepredetermined brightness; if the average brightness or contrast of thetest image satisfies the predetermined brightness, it means that each ofthe setting parameters of the photosensitive element 13 is correct.

In one embodiment, in step S05, the photosensitive element 13automatically adjusts the setting parameters in the calibrationprocedure according to a parameter configuration file, wherein theparameter configuration file stores setting parameters of thephotosensitive element 13. An inspector can update the parameterconfiguration file such that the photosensitive element 13 automaticallyadjusts the setting parameters according to the updated parameterconfiguration file in the calibration procedure, so as to correct anyincorrect setting parameter.

In conclusion of the description above, according to the image detectionscanning system for object surface defects and the image detectionscanning method for object surface defects in one embodiment of thepresent disclosure, a status of an image capturing machine can beautomatically determined before a detection procedure. In someembodiments, the image detection scanning system for object surfacedefects and the image detection scanning method for object surfacedefects of the present disclosure are further capable of performingautomatic or manual calibration in the event of abnormality. In someembodiments, the image detection scanning system for object surfacedefects and the image detection scanning method for object surfacedefects of the present disclosure further use an artificial neuralnetwork system to perform deep learning according to object images of anobject captured, and to build a predictive model for identifying andcategorizing different surface patterns of an object, therebysignificantly enhancing the efficiency of object detection, as well asreducing the possibility of human misjudgment.

The present disclosure is explained by way of the disclosed embodimentsthat are not to be construed as limitations to the present disclosure.Without departing from the spirit and purview of the present disclosure,a person of ordinary skill in the art could make slight modificationsand changes. Therefore, the legal protection of the present disclosureshall be defined by the appended claims.

What is claimed is:
 1. An image detection scanning system for objectsurface defects, suitable for inspecting an object, the systemcomprising: a driver component carrying the object, wherein a surface ofthe object is divided along an extension direction into a plurality ofareas, the driver component sequentially moving one of the plurality ofareas to a detection position; a light source component configured toface the detection position, providing detection light to illuminate thedetection position, a light incident angle of the detection lightrelative to a normal line of the area located at the detection positionbeing less than or equal to 90 degrees; a photosensitive elementconfigured to face the detection position, capturing a detection imageof the area sequentially located at the detection position, andcapturing a test image according to test light before capturing thedetection image of the area, wherein a photosensitive axis of thephotosensitive element is parallel to the normal line or is between thenormal line and the extension direction; and a processor coupled to thephotosensitive element, determining whether a setting parameter of thephotosensitive element is normal according to the test image, andgenerating a warning signal if the setting parameter is abnormal and thephotosensitive element has performed a calibration operationcorresponding to the setting parameter.
 2. The image detection scanningsystem for object surface defects according to claim 1, wherein theprocessor determines whether the setting parameter is normal bydetermining whether average brightness of the test image satisfiespredetermined brightness.
 3. The image detection scanning system forobject surface defects according to claim 1, wherein the settingparameter comprises a photosensitivity value, an exposure value, a focallength value, a contrast setting value, or any combination thereof. 4.The image detection scanning system for object surface defects accordingto claim 1, wherein the processor drives the photosensitive element toperform the calibration operation if the setting parameter is abnormaland the photosensitive element has not yet performed the calibrationoperation.
 5. The image detection scanning system for object surfacedefects according to claim 4, wherein the photosensitive element adjuststhe setting parameter according to a parameter configuration file in thecalibration operation, and captures another test image according to theadjusted setting parameter.
 6. The image detection scanning system forobject surface defects according to claim 1, if the setting parameter isnormal, the processor drives the driver component to sequentially movethe plurality of areas to the detection position, and drives thephotosensitive element to sequentially capture the detection image ofthe area sequentially located at the detection position.
 7. An imagedetection scanning method for object surface defects, suitable forinspecting an object, the method comprising: capturing a test image by aphotosensitive element according to test light; determining whether asetting parameter of the photosensitive element is normal by a processoraccording to the test image; generating a warning signal if the settingparameter is abnormal; performing a detection procedure if the settingparameter is normal; sequentially moving one of a plurality of areas ona surface of the object to the detection position by a driver componentin the detection procedure, wherein the plurality of areas are dividedalong an extension direction of the surface; providing detection lightby a light source component facing the detection position in thedetection procedure, wherein the detection light illuminates thedetection position with a light incident angle of less than or equal to90 degrees relative to a normal line of the area located at thedetection position; configuring the photosensitive element to face thedetection position in the detection procedure, wherein a photosensitiveaxis of the photosensitive element is parallel to the normal line or isbetween the normal line and the extension direction; and capturing adetection image of each of the areas sequentially located at thedetection position by the photosensitive element according to thedetection light in the detection procedure.
 8. The image detectionscanning method for object surface defects according to claim 7, whereinin the step of generating the warning signal, the warning signal isgenerated if the setting parameter is abnormal and the photosensitiveelement has performed a calibration operation corresponding to thesetting parameter.
 9. The image detection scanning method for objectsurface defects according to claim 7, wherein in the step of determiningwhether the setting parameter is normal by the processor according tothe test image, the processor determines whether the setting parameteris normal by determining whether average brightness of the test imagesatisfies predetermined brightness.
 10. The image detection scanningmethod for object surface defects according to claim 7, furthercomprising: driving the photosensitive element to perform a calibrationoperation if the setting parameter is abnormal.
 11. The image detectionscanning method for object surface defects according to claim 10,wherein the step of performing the calibration operation comprises:adjusting the setting parameter of the photosensitive element accordingto a parameter configuration file; and capturing another test image bythe photosensitive element according to the adjusted setting parameter.